The present invention belongs to the art of multiple access communications systems such as, but not limited to, wireless Local Area Networks (Wireless LANS), cellular land-mobile communications systems, mobile satellite communications systems, and memory storage and retrieval devices. Such systems are characterized by at least one fixed base or relay station attempting to maintain communications with a plurality of subscriber stations or terminals that are each assigned a different time slot (TDMA), a different frequency slot (FDMA), or different signature waveform (CDMA), to name a few examples.
In such systems, capacity to support a large number of subscribers is measured in units such as Erlangs per MHz per square kilometer, sum capacity (e.g. the sum of the information data rates—Bits/sec—of all the users in the system). Of primary interest is the maximum number of users that can operate within the system without having to decrease the information rate that they are already accustomed to using or increase the total bandwidth occupied by the system. The capacity can be increased by using more MHz of bandwidth, by reducing the area covered by each base station so that there are more base stations per square kilometer, by decreasing the frequency spacing between channels, and by transmitting more than one signal in the same frequency channel or time slot. However, reducing cell size or reducing the number of signals received by the detector is not always possible or economically feasible. When such action is possible, it increases the infrastructure cost. In addition, some of the above listed solutions increase inter-symbol interference (ISI) and multi-user interference (MUI), also called co-channel interference, that may be caused by a signal being received along with a delayed version thereof caused by a reflection of the signal from an object such as a large building, or receipt of another, weaker signal having the same frequency and meant to be received at a different receiver. In addition, received signals are typically corrupted by additive Gaussian noise.
In order to be able to further accommodate increased traffic, and to make maximum utilization of a traffic channel, multiple interfering signals may be transmitted on the same communication channel and are purposely allowed to interfere with one another. The effects of the resulting multi-user interference is then removed at the receiver by a multi-user detector (MUD). Using a MUD does not require a change in the existing transmitted signaling method, making it an attractive option.
To separate multiple interfering signals transmitted on the same communication channel some unique apriori knowledge of each of the signals is required. For this purpose a parameter estimation unit is required, such as disclosed in co-pending U.S. patent application Ser. No. 09/943,770, filed Aug. 31, 2001, entitled “System For Parameter Estimation And Tracking Of Interfering Digitally Modulated Signals”. The parameter estimation required to attain this apriori knowledge may be done using “blind” parameter estimation, “non-blind” parameter estimation, or parameter estimation with the aid of training sequences. This last method is typically derived using a “training signal” or other knowledge of received signals in a manner well known in the art. The purpose of the parameter estimation unit is to identify and determine parameters associated with each signal that may later be used by the multi-user detector (MUD) to separate each signal from the other interfering signals, regardless of the fact that the signals exist in the same communications bandwidth and at the same instant in time. These parameters might include the received power, the phase of the oscillator which generated each received signal, the timing offset relative to the base station clock, carrier frequency, any frequency offset of the carrier (phase difference), the assigned spreading code, and the structure of multi-path replicas.
To successfully demodulate simultaneously occurring interfering signals, signal processing of the received signals is accomplished utilizing multiuser detection (MUD) techniques. Early work in MUD, described in Multiuser Detection by S. Verdu, Cambridge University Press, 1998 proposed using computationally intense maximum likelihood (ML) exhaustive search techniques to separate the interfering signals. In certain applications, linear MUD detectors with lower computational demands may be used, and such MUD detectors are described by Verdu. However, the reduction in performance, particularly in high-interference situations, is so significant as to make those reduced complexity techniques not applicable. One method of implementing a ML is the well-known decoder known as the Viterbi decoder. A Viterbi decoder is based upon the Viterbi algorithm and performs a breadth first decoding search of all paths through an entire code tree (or trellis, which is a more compact representation of the code tree) by extending paths through the tree and the entire tree is searched. The complexity of the maximum likelihood (ML) Viterbi decoder in the context of many applications is prohibitively high.
The M-algorithm is a tree-pruning technique that approximates the operation of a ML Viterbi decoder at reduced complexity. The M-algorithm is a breadth first decoding algorithm, but with the M algorithm only the best M paths are retained at each level in the tree. This reduced tree search, referred to as “tree pruning”, reduces the number of calculations that must be made and therefore speeds the overall tree processing. The M-algorithm is described in greater detail further in the specification.
Viterbi algorithm decoders and M algorithm decoders are also well known in the art as maximum likelihood decoders which can be used in systems that employ error correcting codes, such as convolutional codes tree codes, and a variety of other codes, all of which can be generally characterized by a tree. The basic concept of these decoders can be described as correlating all possible transmitted sequences with the received sequence and then choosing as the “best” or “maximum likelihood” path the sequence whose correlation is a maximum.
A tree consists of a sequence of concatenations of a so-called tree diagram, or state transition diagram. The tree diagram defines, for each code state, which next state or states the encoder is allowed to transition to. The allowable transitions from one state to a next state are limited. Each possible transition from one state to a next state in a tree is called a branch. Each branch, therefore, corresponds to a subset. A sequence of signal points selected from a sequence of interconnected branches is called a path through the tree.
Transmitted signal points are displaced in signal space due to noise and channel-induced distortion, and a receiver may use a Viterbi algorithm decoder or an M algorithm decoder, operating on a received version of the transmitted signal points, to perform the aforementioned maximum likelihood sequence detection or an approximation of ML sequence detection, respectively. Based on the received version of the transmitted signal points and the knowledge of the tree code used by the encoder, the decoder determines the most likely sequence of signal points that was actually transmitted. The decoder performs this function by forming a decision as to what was the most likely transmitted signal point that would have caused the encoder to transition into a next state of the code. The technique works on concepts that can be modeled as a tree code. In the case of interfering signals, a tree can be formed that represents all possible choices of the transmitted values for all signals. That is, error correction coding is not necessarily assumed for tree decoding and doesn't necessarily dictate the formation of the tree. Rather, the tree is formed by the fact that different hypotheses for the received sequences are possible.
More particularly, a Viterbi algorithm decoder, an M algorithm decoder, or any other tree-search decoder forms paths through a tree by keeping track of so-called “metrics”. A branch metric, a function of the received version of the signal point, is calculated for each current-to-next-state transition associated with each branch in the tree diagram. Every path through the tree which leads into a state has an associated path metric which is a function of the sum of the branch metrics for the branches that make up that particular path. Further, a path entering a current state may be extended through the tree and enter a next state by including a branch representing an allowed transition from the current state to the next state. The path metric for such an extended path is a function of the sum of (a) the path metric associated with the path as it entered the current state and (b) the branch metric associated with the included branch.
The Viterbi decoder compares the path metrics of the different paths entering a state and retains as one of the aforementioned surviving paths the path with the smallest path metric. All other paths entering that state are discarded. The surviving paths are used by the decoder to make a final decision as to the value of an earlier transmitted signal point.
To reduce the complexity of the tree search, thereby increasing the speed of testing multiple hypotheses, shortcuts may be deliberately taken in the processing with a tree decoder. For instance, the M-algorithm prunes the tree by retaining, at every stage in the tree, the best M paths through the tree at each level in the tree. The computational complexity of a tree search is directly related to the number of hypotheses which must be tested, i.e. the number of paths through the tree which must be examined. For example, for an ML multi-user detector for which there are K interfering signals and which uses the Viterbi algorithm, the computational complexity is on the order of 2K for each symbol interval. For the M-algorithm, the complexity is on the order of K1.2 for each symbol interval. The reduction in complexity by using the M-algorithm is considerable, but not for very large values of K or for high data rates. In addition, tree pruning carries with it the risk that the correct path through the tree is eliminated from consideration, which causes a decoding error. Judicious pruning is required. For the M-algorithm, as M is decreased, the complexity is reduced but the probability of incorrect pruning increases. That is, the need for accuracy limits the reduction in complexity that is feasible. The M-algorithm is described in greater detail further in the Summary of the Invention. See also U.S. Pat. No. 6,151,370 issued Nov. 21, 2000 which describes the M-algorithm. Tree pruning techniques also apply to maximum a posteriori (MAP) decoders.
The process used to decode turbo codes, known as the “turbo principal,” may be used as an alternative to ML decoding in systems other than turbo coded systems. Because the turbo principal is used in the multi-user detector (referred to as TurboMUD) described in this invention even though it does not employ turbo codes, turbo decoding is now described in the context of turbo codes. However, turbo decoding, or the turbo principal, may be used whenever the system chain up to the receiver contains either serial or parallel concatenated components that mathematically resemble codes. Turbo codes are forward error control codes that are generated using recursive systematic encoders operating on different permutations of the same code information bits to improve the performance of a transmission channel. Turbo decoding involves an iterative algorithm in which probability estimates of the code information bits that are derived by one decoder for the coded information bits being processed in that decoder are fed back to the other decoder as a priori information that can be used in processing by that decoder. Each iteration of decoding of the code information bits through the two decoders generally increases the reliability of the probability estimates. This iterative feedback and decoding process continues, decoding the code information bits a finite number of times, and a decision is made based on the final probability estimates that the bits represent the transmitted data and can be used to make reliable decoding decisions. The turbo decoder operates on one block of coded data bits, or symbols, at a time, passing the revised estimates between the component decoders until processing of that block is complete. One complete pass through both component decoders in the turbo decoder by a block of coded bits is referred to as a decoding iteration; a typical number of iterations required for adequate bit error performance is three to eight.
An arrangement for performing a termination checking procedure, preferably performed after each iteration of decoding, is to determine if a minimal absolute probability value associated with any of the bits in the packet has been reached. Such an arrangement is taught in U.S. Pat. No. 6,182,261. When the minimal absolute probability value is above a predetermined threshold, indicating that all of the bits have been assigned either the value “1” or “0” with relatively high probability, the iterative turbo decoding process is terminated. An alternative to this termination checking procedure is to perform a pre-determined number of iterations for each block of information.
More particularly, with iterative turbo decoding, rather than determining immediately whether received code information bits are either a zero or one, the receiver assigns each code information bit a value on a multi-level scale representative of the probability that the bit is one. A common scale, referred to as log-likelihood ratio (LLR) values, represents each bit by an integer in an implementation-specific range, for instance in the range (−32, +31). For this example integer range, the value of +31 signifies that the transmitted bit was a zero with very high probability, and the value of −32 signifies that the transmitted bit was a one, with very high probability. An LLR value of zero indicates that the bit value is indeterminate. Stated another way, those bits which have a probability indicating that they are closer to one (for example, between 0 and +31 on the scale described above) are tentatively assigned a value of zero, and the rest of the bits (between −32 and 0) are tentatively assigned a value of one. Furthering the example, an LLR value of +31 means that the transmitted bit value is zero with a probability of 31/62+0.5=1, and the probability that the transmitted bit value is one is 0.5−31/62=0. An LLR probability of 16 means that the probability of bit value zero is approximately 0.75 and the probability of bit value one is approximately 0.25. When a probability is equal to 0.5, it means that either bit value (zero or one) is equally likely. The probabilities then, and the corresponding LLR values, indicate the confidence with which the decoder is making the bit decision.
Data represented on the multi-level scale described in the previous paragraph is referred to as “soft data,” and the iterative decoding performed is usually soft-in/soft-out, i.e., the decoding process receives a sequence of inputs corresponding to probabilities for the code information bit values and provides as output corrected probabilities taking into account constraints of the code information bits. Generally, a decoder which performs iterative decoding, uses soft data from former iterations to decode the soft data read by the receiver. A method of iterative decoding is described, for example, in U.S. Pat. No. 5,563,897.
The turbo principal as described above is a powerful alternative to ML or MAP decoders but, in general is extremely computationally intensive. The component decoders contained within the turbo decoder, may employ shortcut techniques that reduce the complexity. The component decoders themselves typically contain ML or MAP tree search algorithms such as Viterbi decoders, M-algorithm decoders or other tree search algorithms. The decreased incremental performance of each component that comes as a cost of reduced incremental (i.e. per-iteration) complexity is compensated by iterating. The component decoders contained within the turbo-decoder exploit different relationships between the signals, allowing for performance gains as the number of iterations increases. That is, an iterative decoder using the turbo principal produces improved overall performance when compared to a non-iterative reduced complexity tree search algorithm of similar complexity. However, processing the interfering signals multiple times, i.e. iterating, to maintain the performance level as measured by bit error rates mitigates the complexity reduction gains achieved by shortcuts within the component decoders of the turboMUD. A tradeoff of complexity versus performance and complexity versus processing speed remains.
To further improve the performance of a communication system, some coding schemes include interleavers at the transmitter, which mix up the order of the bits in each packet of bits during encoding. Thus, when interference destroys a few adjacent bits during transmission, the effect of the interference is spread out over the entire original packet and can more readily be overcome by the decoding process. Other improvements may include multiple-component codes which include coding the packet more than once in parallel or in series. However, as this invention is concerned with operation at the receiver, the interleavers included in the receiver are only the interleavers and de-interleavers that are necessary to reverse the operation of any interleaving done at the transmitter.
In short, despite all the decoding processing gains in the art there is still a need for an improved method and apparatus for signal processing simultaneously occurring, interfering signals to speed the decoding processing and allow for acceptable detection performance at realtime operational speeds.